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Archive of posts filed under the Stan category.

A normalizing flow by any other name

Another week, another nice survey paper from Google. This time: Papamakarios, G., Nalisnick, E., Rezende, D.J., Mohamed, S. and Lakshminarayanan, B., 2019. Normalizing Flows for Probabilistic Modeling and Inference. arXiv 1912.02762. What’s a normalizing flow? A normalizing flow is a change of variables. Just like you learned way back in calculus and linear algebra. Normalizing […]

Making differential equation models in Stan more computationally efficient via some analytic integration

We were having a conversation about differential equation models in pharmacometrics, in particular how to do efficient computation when fitting models for dosing, and Sebastian Weber pointed to this Stancon presentation that included a single-dose model. Sebastian wrote: Multiple doses lead to a quick explosion of the Stan codes – so things get a bit […]

MRP Conference at Columbia April 3rd – April 4th 2020

The Departments of Statistics and Political Science and Institute for Social and Economic Research and Policy at Columbia University are delighted to invite you to our Spring conference on Multilevel Regression and Poststratification. Featuring Andrew Gelman, Beth Tipton, Jon Zelner, Shira Mitchell, Qixuan Chen and Leontine Alkema, the conference will combine a mix of cutting […]

StanCon 2020: August 11-14. Registration now open!

The 5th Stan Conference will be at Oregon State University on August 11-14, 2020. Register here: https://stancon2020.eventbrite.com The four-day event will be two days of tutorials and two days of talks, open discussions, and statistical modeling. Up-to-date information can be found at https://mc-stan.org/events/stancon2020. Registration Fees Registration fees cover the entire 4-day conference. This includes coffee […]

Causal inference in AI: Expressing potential outcomes in a graphical-modeling framework that can be fit using Stan

David Rohde writes: We have been working on an idea that attempts to combine ideas from Bayesian approaches to causality developed by you and your collaborators with Pearl’s do calculus. The core idea is simple, but we think powerful and allows some problems previously that only had known solutions with the do calculus to be […]

Hey—the New York Times is hiring an election forecaster!

Chris Wiggins points us to this job opening: Staff Editor – Statistical Modeling The New York Times is looking to increase its capacity for statistical projects in the newsroom, especially around the 2020 election. You will help produce statistical forecasts for election nights, as part of The Times’s ambitious election results operation. That operation is […]

Exciting postdoc opening in spatial statistics at Michigan: Coccidioides is coming, and only you can stop it!

Jon Zelner is an collaborator who does great work on epidemiology using Bayesian methods, Stan, Mister P, etc. He’s hiring a postdoc, and it looks like a great opportunity: Epidemiological, ecological and environmental approaches to understand and predict Coccidioides emergence in California. One postdoctoral fellow is sought in the research group of Dr. Jon Zelner […]

How to “cut” using Stan, if you must

Frederic Bois writes: We had talked at some point about cutting inference in Stan (that is, for example, calibrating PK parameters in a PK/PD [pharmacokinetic/pharmacodynamic] model with PK data, then calibrating the PD parameters, with fixed, non updated, distributions for the PK parameters). Has that been implemented? (PK is pharmacokinetic and PD is pharmacodynamic.) I […]

Criminologists be (allegedly) crimin’ . . . and a statistical New Year’s toast for you.

Someone who wishes to remain anonymous points us to this video, writing: It has to do with Stewart at FSU, in criminology. Couldn’t produce a survey that was the basis for 5 papers, all retracted. FSU though still failed to do complete investigation. The preliminary investigation had a 3 person panel, 2 of whom were […]

DAGS in Stan

Macartan Humphries writes: As part of a project with Alan Jacobs we have put together a package that makes it easy to define, update, and query DAG-type causal models over binary nodes. We have a draft guide and illustrations here. Now I know that you don’t care much for the DAG approach BUT this is […]

Fitting big multilevel regressions in Stan?

Joe Hoover writes: I am a social psychology PhD student, and I have some questions about applying MrP to estimation problems involving very large datasets or many sub-national units. I use MrP to obtain sub-national estimates for low-level geographic units (e.g. counties) derived from large data (e.g. 300k-1 million+). In addition to being large, my […]

Beautiful paper on HMMs and derivatives

I’ve been talking to Michael Betancourt and Charles Margossian about implementing analytic derivatives for HMMs in Stan to reduce memory overhead and increase speed. For now, one has to implement the forward algorithm in the Stan program and let Stan autodiff through it. I worked out the adjoint method (aka reverse-mode autodiff) derivatives of the […]

How many Stan users are there?

This is an interesting sampling or measurement problem that came up in a Discourse thread started by Simon Maskell: It seems we could look at a number of pre-existing data sources (eg discourse views and contributors, papers, StanCon attendance etc) to inform an inference of how many people use Stan (and/or use things that use […]

Field goal kicking—like putting in 3D with oblong balls

Putting Andrew Gelman (the author of most posts on this blog, but not this one), recently published a Stan case study on golf putting [link fixed] that uses a bit of geometry to build a regression-type model based on angles and force. Field-goal kicking In American football, there’s also a play called a “field goal.” […]

Hey—the 2nd-best team in baseball is looking for a Bayesian!

Sarah Gelles writes: We are currently looking to hire a Bayesian Statistician to join the Houston Astros’ Research & Development team. They would join a growing, cutting-edge R&D team that consists of analysts from a variety of backgrounds and which is involved in all key baseball decisions at the Astros. Here’s a link to the […]

Some recent progress in the Stan community

Bob writes in with a partial list of recent developments in the Stan community. Governance: The interim Stan governing body stepped down and were replaced with a new board elected by the developer community. Funding: Stan receives millions of dollars annually in grants, gifts, and in-kind contributions across its global developer base. Releases: Stable quarterly […]

Econometrics postdoc and computational statistics postdoc openings here in the Stan group at Columbia

Andrew and I are looking to hire two postdocs to join the Stan group at Columbia starting January 2020. I want to emphasize that these are postdoc positions, not programmer positions. So while each position has a practical focus, our broader goal is to carry out high-impact, practical research that pushes the frontier of what’s […]

Stan saves Australians $20 billion

Jim Savage writes: Not sure if you knew, but Stan was used in the Australian Productivity Commission’s review of the Australian retirement savings system. Their review will likely affect the regulation on $2 trillion of retirement savings, possibly saving Australians around $20-50 billion in fees over the next decade. OK, we can now officially say […]

Padres need Stan

Cody Zupnick writes: I’m working in baseball research for the San Diego Padres, and we’re looking for new people, potentially with Stan experience. Would you mind seeing if any of your readers have any interest? Cool!

Hey, Stan power users! PlayStation is Hiring.

Imad writes: The Customer Lifecycle Management team at PlayStation is looking to hire a Senior Data Modeler (i.e. Data Scientist). DM me if you like building behavioral models and working with terabytes of data. You’ll have the opportunity use whatever tools you want (e.g. Stan) to build your models. I’m not into videogames myself, but […]